In the past decade, various diversified information application services are provided owing to the development of network technologies and the emergence of cloud service industries. However, as the technologies of the Internet of Things (IoT) emerge, more and more apparatuses are connected to the network. When a large number of IoT apparatuses are connected to the network at the same time, a great amount of network resources (e.g., bandwidth, storage space, and CPU computation capability) is consumed, which brings challenges to cloud computing.
Edge computing is proposed to reduce the loading of cloud computing. Specifically, edge computing is based on the concept of doing computation nearby. In other words, computation is carried out in a local network closer to the data source to avoid returning the data to a cloud server as much as possible and thereby reduce the waiting time of receiving and transmitting data from and to the cloud server as well as the cost for network bandwidth. Besides, properly arranging a plurality of edge nodes may facilitate the scalability and reliability.
Nevertheless, not all the data are suitable to be configured locally for computation. For example, some data may require further analysis and judgment, and need to be transmitted to the cloud server for further processing or for long-term accessibility. Thus, network nodes need to be appropriately configured in correspondence with current job requirements to process all the jobs.